234 citations · 350 across the 11 of their papers we have counts for
12 papers
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture
Anh Tran, Mike Eldred, Tim Wildey +3
High-fidelity complex engineering simulations are highly predictive, but also computationally expensive and often require substantial computational efforts. The mitigation of compu…
Differential network analysis and graph classification: a glocal approach
Giuseppe Jurman, Michele Filosi, Samantha Riccadonna +2
Based on the glocal HIM metric and its induced graph kernel, we propose a novel solution in differential network analysis that integrates network comparison and classification task…
DTW-MIC Coexpression Networks from Time-Course Data
Samantha Riccadonna, Giuseppe Jurman, Roberto Visintainer +2
When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. Howe…
Stability Indicators in Network Reconstruction
Giuseppe Jurman, Michele Filosi, Roberto Visintainer +2
The number of algorithms available to reconstruct a biological network from a dataset of high-throughput measurements is nowadays overwhelming, but evaluating their performance whe…
Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers
Davide Albanese, Michele Filosi, Roberto Visintainer +3
We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large dataset…
mlpy: Machine Learning Python
Davide Albanese, Roberto Visintainer, Stefano Merler +3
mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning…
The HIM glocal metric and kernel for network comparison and classification
Giuseppe Jurman, Roberto Visintainer, Michele Filosi +2
Due to the ever rising importance of the network paradigm across several areas of science, comparing and classifying graphs represent essential steps in the networks analysis of co…
Biological network comparison via Ipsen-Mikhailov distance
Giuseppe Jurman, Samantha Riccadonna, Roberto Visintainer +1
Highlighting similarities and differences between networks is an informative task in investigating many biological processes. Typical examples are detecting differences between an…
A machine learning pipeline for discriminant pathways identification
Annalisa Barla, Giuseppe Jurman, Roberto Visintainer +4
Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Re…
Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms
Marco Grimaldi, Giuseppe Jurman, Roberto Visintainer
Motivation :Reconstructing the topology of a gene regulatory network is one of the key tasks in systems biology. Despite of the wide variety of proposed methods, very little work h…
An introduction to spectral distances in networks (extended version)
Giuseppe Jurman, Roberto Visintainer, Cesare Furlanello
Many functions have been recently defined to assess the similarity among networks as tools for quantitative comparison. They stem from very different frameworks - and they are tune…
Algebraic Comparison of Partial Lists in Bioinformatics
Giuseppe Jurman, Samantha Riccadonna, Roberto Visintainer +1
The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classificatio…